Aug 2026· Journal of Economics, Finance and Accounting Studies· Vol 8, pp. 25-30· 0 citations· 17 references
TL;DR
The findings support risk-calibrated pre-deployment oversight while highlighting comparatively less consistent public disclosure of lifecycle monitoring after deployment.
Abstract
Generative artificial intelligence (GenAI) is increasingly embedded in marketing content creation, personalization, customer communication, advertising, and multi-channel campaign execution. Although GenAI can improve speed and scalability, its use creates risks involving accuracy, privacy, intellectual property, bias, compliance, brand consistency, and consumer protection. This study develops and empirically examines a risk-calibrated governance framework for cross-functional marketing projects. The research combines an integrative review and thematic synthesis of relevant scholarly literature with an archival content analysis of official AI-governance disclosures from a purposive sample of large U.S. public companies across technology, finance, and consumer/media sectors. Six governance gates were coded as publicly disclosed or not observed: risk classification, input and data governance, output quality review, legal and ethical compliance, approval and human oversight, and post-deployment monitoring and escalation. The mean governance disclosure score was 5.27 out of 6. Legal and ethical compliance was universally disclosed within the sample, while data governance, risk classification, output quality review, and approval or human oversight were also widely observed. Post-deployment monitoring and escalation was the least consistently disclosed governance stage. Fisher's exact test showed that firms disclosing risk classification were more likely to disclose approval or human-oversight controls (odds ratio = 36.00, p = .038). The corresponding association between risk classification and post-deployment monitoring was positive but not statistically significant (odds ratio = 10.67, p = .117). Governance disclosure scores did not differ significantly across sectors (Kruskal-Wallis H = 0.067, p = .967). The findings support risk-calibrated pre-deployment oversight while highlighting comparatively less consistent public disclosure of lifecycle monitoring after deployment
Artificial intelligence (AI), and generative AI in particular, is rapidly reshaping how financial services firms operate, from investment research and portfolio analytics to marketing communications, financial promotions, compliance, and risk management. While the potential efficiency and scalability gains are significant, so too are the regulatory, operational, and governance risks associated with AI adoption. This paper examines how financial services firms can strike a practical balance between innovation and risk by embedding AI within disciplined governance, compliance, and supervisory frameworks. Using marketing compliance and financial promotions as a central case study, the paper explores how AI can be deployed as a pre-review and triage mechanism — supporting global consistency, scalability, and time-to-market — while preserving human judgment, accountability, and regulatory defensibility. Drawing on regulatory expectations and practical implementation considerations, the paper outlines a pragmatic approach to AI governance designed for complex, multi-jurisdictional organisations. This article is also included in The Business & Management Collection which can be accessed at http://hstalks.com.business/.
Johanna Anders· Journal of financial complia...· 0 citations
Artificial intelligence (AI) is entering audit workflows while sustainability reporting expands the evidence subject to professional evaluation. This exploratory study examines how the UK Big Four publicly describe safeguards that keep AI-assisted work human-led, reviewable and accountable. The complete 2024 transparency-report cross-section was coded against seven pre-specified dimensions and summarized in an AI–Judgment Governance Disclosure Index (AI-JGDI). The index measures AI disclosure governance—the completeness of public accountability commitments—not internal control effectiveness. Firm evidence is reported with page-level passages and a decision-level coding log; the single-coder design remains a substantive limitation. The results are compared only as external context with recent inspection outcomes published by the UK Financial Reporting Council. All four firms disclose deployed AI capabilities and retained human responsibility; disclosure is most complete for oversight, accountability and learning, and least complete for AI-specific validation and engagement-level traceability. Sensitivity analysis supports the broad cross-firm pattern but not a precise ranking. The audit evidence is empirical; the sustainability-assurance extension is analytical. Across the four reports, AI disclosure governance and sustainability-assurance disclosures remain largely parallel, with no explicit engagement-level methodological link identified.
Radosveta Krasteva-Hristova· Journal of Risk and Financia...· 0 citations
Across U.S. higher education, adoption of generative artificial intelligence (GenAI) is expanding faster than many campus governance systems can guide, monitor, or audit. This paper offers an equity-first, risk-calibrated policy playbook for higher education leaders by integrating equity-centered leadership perspectives with the National Institute of Standards and Technology Artificial Intelligence Risk Management Framework. It specifies governance mechanisms, including risk-tiered use-case portfolios, clear decision rights, procurement and vendor-assurance standards, and auditable documentation such as impact assessments. These mechanisms are anchored in U.S. accountability concerns, including privacy, accessibility, nondiscrimination, and due process. Drawing on a policy-design analysis of federal guidance, institutional policy syntheses, and illustrative state/system levers, the paper identifies recurring governance failure modes, including shadow AI use and decision-support tools that become determinative in practice. It specifies proportionate controls campuses can implement without building a parallel bureaucracy. It concludes with a concise measurement approach for internal improvement and public-facing accountability.
Natasha N. Johnson, Thaddeus L. Johnson· Educational Policy· 0 citations
It is concluded that a holistic AI compliance framework integrating data governance, bias mitigation strategies, legal oversight, cybersecurity, and ethical accountability is essential for ensuring responsible and trustworthy AI deployment in high-stakes environments.
Joy Oluchi Nwachukwu, Thaddaeuse Odhiambo, Dorcas Akorkor Apaflo et al.· Journal of Economic, Finance...· 0 citations
Generative artificial intelligence (GenAI) is increasingly embedded in cross-border e-commerce supply chains, where digital platforms support automated content generation, product recommendation, customer interaction, supplier evaluation, compliance automation, and sustainability reporting. While these applications create opportunities for efficiency and innovation, they also introduce ethical, governance, and sustainability risks that extend beyond individual algorithms or isolated organizational processes. This study conceptualizes system-level risk as risk that emerges and propagates across interconnected data pipelines, model architectures, platform functions, organizational actors, supply-chain relationships, and cross-jurisdictional regulatory environments. Drawing on a PRISMA 2020-based systematic literature review of 66 peer-reviewed and policy-relevant publications, the article develops a structured taxonomy of GenAI-related risks and an integrated conceptual governance framework for cross-border e-commerce supply chains. The review identifies five core risk domains: bias and fairness, privacy and data governance, misinformation and manipulation, accountability and liability, and environmental sustainability. The findings suggest that these risks often interact and reinforce one another across data, model, application, organizational, and regulatory layers, rather than operating as separate technical problems. The study further maps these risk domains to governance mechanisms discussed in the literature, including explainable AI, privacy-preserving and federated learning approaches, auditability mechanisms, human-in-the-loop oversight, regulatory alignment, and green AI strategies. However, the review further indicates that these mechanisms are supported by different levels of evidence and should be understood not as universally validated solutions, but as governance enablers. The proposed framework does not seek to replace existing AI governance models, but rather complements and extends them by focusing on GenAI risk propagation in cross-border platform ecosystems, where technical design, organizational responsibility, regulatory fragmentation, and sustainability obligations intersect. The article contributes to the literature by offering a system-level synthesis of GenAI governance risks and by outlining a diagnostic framework that may support researchers, platform operators, and policymakers in assessing and governing GenAI-enabled cross-border e-commerce infrastructures. The limitations of the study include its conceptual nature, its reliance on English-language indexed literature, and the absence of primary empirical validation.
The rapid diffusion of artificial intelligence (AI) is reshaping corporate reporting and sustainability practices, yet empirical evidence on how AI adoption improves environmental, social, and governance disclosure quality remains limited. This study investigates the effect of artificial intelligence adoption (AIA) on ESG disclosure quality (ESGQ) among manufacturing firms in G7 economies and examines how organizational and institutional conditions shape this relationship. By focusing on advanced economies with strong regulatory frameworks and high digital readiness, the study provides a suitable setting to assess technology‐driven sustainability outcomes. Using a panel dataset of 5600 firm‐year observations from 2017 to 2024, the analysis employs distribution‐sensitive estimation techniques and complementary robustness tests to capture heterogeneity and mitigate endogeneity concerns. The findings reveal a consistently positive relationship between AIA and ESGQ across firms with varying levels of disclosure quality, indicating that AI enhances the accuracy, timeliness, and credibility of sustainability reporting. Moreover, stakeholder engagement, regulatory pressure, and institutional ownership significantly strengthen this relationship, highlighting the importance of governance structures and institutional pressures in translating technological capability into substantive disclosure improvements. The study contributes theoretically by integrating stakeholder theory, the resource‐based view, and institutional theory to explain how AI functions as a strategic capability whose sustainability impact is contingent on governance and institutional contexts. Practically, the results underscore the need for managers to align AI investments with stakeholder engagement and regulatory compliance, while policymakers and regulators are encouraged to promote AI‐enabled, transparent ESG reporting frameworks that support responsible and sustainable corporate behavior in the digital era.
Desmond Bayong, Dejun Zhou, A. Agyemang· Business Strategy and the En...· 0 citations
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